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Publication Detail
Individualized prediction of illness course at the first psychotic episode: a support vector machine MRI study.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Journal Article
  • Authors:
    Mourao-Miranda J, Reinders AATS, Rocha-Rego V, Lappin J, Rondina J, Morgan C, Morgan KD, Fearon P, Jones PB, Doody GA, Murray RM, Kapur S, Dazzan P
  • Publication date:
    05/2012
  • Pagination:
    1037, 1047
  • Journal:
    Psychol Med
  • Volume:
    42
  • Issue:
    5
  • Status:
    Published
  • Country:
    England
  • PII:
    S0033291711002005
  • Language:
    eng
  • Keywords:
    Adult, Brain, Brain Mapping, Cohort Studies, Disease Progression, Female, Follow-Up Studies, Humans, Image Processing, Computer-Assisted, Individuality, Magnetic Resonance Imaging, Male, Observer Variation, Predictive Value of Tests, Psychotic Disorders, Reproducibility of Results, Support Vector Machine
Abstract
BACKGROUND: To date, magnetic resonance imaging (MRI) has made little impact on the diagnosis and monitoring of psychoses in individual patients. In this study, we used a support vector machine (SVM) whole-brain classification approach to predict future illness course at the individual level from MRI data obtained at the first psychotic episode. METHOD: One hundred patients at their first psychotic episode and 91 healthy controls had an MRI scan. Patients were re-evaluated 6.2 years (s.d.=2.3) later, and were classified as having a continuous, episodic or intermediate illness course. Twenty-eight subjects with a continuous course were compared with 28 patients with an episodic course and with 28 healthy controls. We trained each SVM classifier independently for the following contrasts: continuous versus episodic, continuous versus healthy controls, and episodic versus healthy controls. RESULTS: At baseline, patients with a continuous course were already distinguishable, with significance above chance level, from both patients with an episodic course (p=0.004, sensitivity=71, specificity=68) and healthy individuals (p=0.01, sensitivity=71, specificity=61). Patients with an episodic course could not be distinguished from healthy individuals. When patients with an intermediate outcome were classified according to the discriminating pattern episodic versus continuous, 74% of those who did not develop other episodes were classified as episodic, and 65% of those who did develop further episodes were classified as continuous (p=0.035). CONCLUSIONS: We provide preliminary evidence of MRI application in the individualized prediction of future illness course, using a simple and automated SVM pipeline. When replicated and validated in larger groups, this could enable targeted clinical decisions based on imaging data.
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